Modern TCM understands the pathogenesis of blood glucose fluctuation mainly from the viscera (spleen, kidney, liver, bile, small intestine), middle energizer, "yin fire theory", "Xuanfu depression" and "qi transformation". In clinic, blood glucose fluctuation is mainly divided into four syndrome types: qi-yin deficiency syndrome, yin-yang deficiency syndrome, exuberance of fire and heat syndrome, and heat stagnation in liver-stomach syndrome. TCM compounds can improve blood glucose fluctuation by improving insulin resistance, regulating intestinal microflora, promoting the secretion of glucagon-like peptide-1, and fighting oxidative stress.

Objective:To compare the performances of different time series models in predicting COVID-19 in different countries.Methods:We collected the daily confirmed case numbers of COVID-19 in the USA, India, and Brazil from April 1 to September 30, 2020, and then constructed an autoregressive integrated moving average (ARIMA) model and a recurrent neural network (RNN) model, respectively. We applied the mean absolute percentage error (MAPE) and root mean square error (RMSE) to compare the performances of the two models in predicting the case numbers from September 21 to September 30, 2020.Results:For the ARIMA models applied in the USA, India, and Brazil, the MAPEs were 13.18%, 9.18%, and 17.30%, respectively, and the RMSEs were 6 542.32, 8 069.50, and 3 954.59, respectively. For the RNN models applied in the USA, India, and Brazil, the MAPEs were 15.27%, 7.23% and 26.02%, respectively, and the RMSEs were 6 877.71, 6 457.07, and 5 950.88, respectively.Conclusions:The performance of the prediction models varied with country. The ARIMA model had a better prediction performance for COVID-19 in the USA and Brazil, while the RNN model was more suitable in India.